Source apportionment of ambient fine and coarse particulate matter polycyclic aromatic hydrocarbons at the Bertha Ganter-Fort McKay community site in the Oil Sands Region of Alberta, Canada
Bibliographic record
Abstract
A comprehensive filter-based particulate matter polycyclic aromatic hydrocarbon (PAH) source apportionment study was conducted at the Wood Buffalo Environmental Association Bertha Ganter-Fort McKay (BGFM) community monitoring station from 2014 to 2015 to quantify ambient concentrations and identify major sources. The BGFM station is located in close proximity to several surface oil sands production facilities and was previously found to be impacted by their air emissions. 24-hour integrated PM2.5 and PM10–2.5 samples were collected on a 1-in-3-day schedule yielding 108 complete organic/inorganic filter sets for source apportionment modeling. During the study period PM2.5 averaged 8.6 ± 11.8 μg m−3 (mean ± standard deviation), and PM10–2.5 averaged 8.5 ± 9.5 μg m−3. Wind regression analysis indicated that the oil sands production facilities were significant sources of PM2.5 mass and black carbon (BC), and that wildland fires were a significant source of the highest PM2.5 (>10 μg m−3) and BC events. A six-factor positive matrix factorization (PMF) model solution explained 95% of the measured PM2.5 and 78% of the measured ΣPAH. Five sources significantly contributed to PM2.5 including: Biomass Combustion (3.57 μg m−3; 40%); Fugitive Dust (1.86 μg m−3; 28%); Upgrader Stack Emissions (1.44 μg m−3; 21%); Petrogenic PAH (1.20 μg m−3; 18%); and Transported Aerosol (0.43 μg m−3 and 6%). However, the analysis indicated that only the pyrogenic PAH source factor significantly contributed (78%) to the measured ΣPAH. A five-factor PMF model dominated by fugitive dust sources explained 98% of PM10–2.5 mass and 86% of the ΣPAH. The predominant sources of PM10–2.5 mass were (i) Haul Road Dust (4.82 μg m−3; 53%), (ii) Mixed Fugitive Dust (2.89 μg m−3; 32%), (iii) Fugitive Oil Sand (0.88 μg m−3; 10%), Mobile Sources (0.23 μg m−3; 2%), and Organic Aerosol (0.06 μg m−3; 1%). Only the Organic Aerosol source significantly contributed (86%) to the measured ΣPAH.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".